README.md

---
license:
- other
pretty_name: >-
  python copilot image training using class knowledge graphs updated 2024-01-27
dataset_info:
- config_name: v1_transformers_examples_pytorch
  splits:
  - name: v1_transformers_examples_pytorch
- config_name: v2_pytorch_torch_distributed_fsdp
  splits:
  - name: v2_pytorch_torch_distributed_fsdp
- config_name: v3_deepspeed_deepspeed_runtime
  splits:
  - name: v3_deepspeed_deepspeed_runtime
- config_name: v4_fused_gelu_testing_src
  splits:
  - name: v4_fused_gelu_testing_src
- config_name: v5_unsloth_unsloth_models
  splits:
  - name: v5_unsloth_unsloth_models
- config_name: v6_blip_models
  splits:
  - name: v6_blip_models
- config_name: v7_text_generation_inference_server_text_generation_server
  splits:
  - name: v7_text_generation_inference_server_text_generation_server
- config_name: v8_spark_python_pyspark_pandas_plot
  splits:
  - name: v8_spark_python_pyspark_pandas_plot
- config_name: view_schema
  splits:
  - name: view_schema
configs:
- config_name: v1_transformers_examples_pytorch
  data_files:
  - split: v1_transformers_examples_pytorch
    path: train/train-0002-transformers-examples-pytorch.parquet
- config_name: v2_pytorch_torch_distributed_fsdp
  data_files:
  - split: v2_pytorch_torch_distributed_fsdp
    path: train/train-0003-pytorch-torch-distributed-fsdp.parquet
- config_name: v3_deepspeed_deepspeed_runtime
  data_files:
  - split:  v3_deepspeed_deepspeed_runtime
    path: train/train-0004-deepspeed-deepspeed-runtime.parquet
- config_name: v4_fused_gelu_testing_src
  data_files:
  - split: v4_fused_gelu_testing_srck
    path: train/train-0005-fused-gelu-testing-src.parquet
- config_name: v5_unsloth_unsloth_models
  data_files:
  - split: v5_unsloth_unsloth_models
    path: train/train-0006-unsloth-unsloth-models.parquet
- config_name: v6_blip_models
  data_files:
  - split: v6_blip_models
    path: train/train-0007-blip-models.parquet
- config_name: v7_text_generation_inference_server_text_generation_server
  data_files:
  - split: v7_text_generation_inference_server_text_generation_server
    path: train/train-0008-text-generation-inference-server-text_generation_server.parquet
- config_name: v8_spark_python_pyspark_pandas_plot
  data_files:
  - split: v8_spark_python_pyspark_pandas_plot
    path: train/train-0009-spark-python-pyspark-pandas-plot.parquet
- config_name: view_schema
  data_files:
  - split: view_schema
    path: files/lok-python-copilot-image.class-v1_00003555.parquet
size_categories:
- 100K<n<1M
tags:
- python-copilot
- python-coding
- python-architecture
- knowledge-graphs
- multimodal
- text-image-audio
- fine-tuning
- training
- question-answering
- image-knowledge-graph
- alpaca
- mp3
- png
- text
- instruct
- class
- classes
# supported task_categories
# text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, other
task_categories:
- text-to-image
- image-to-image
- question-answering
# supported task_ids
# acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
task_ids:
- parsing
---

## Python Copilot Image Training using Class Knowledge Graphs

This dataset is a subset of the matlok python copilot datasets. Please refer to the [Multimodal Python Copilot Training Overview](https://huggingface.co/datasets/matlok/multimodal-python-copilot-training-overview) for more details on how to use this dataset.

### Details

Each row contains a png file in the **dbytes** column.

- Rows: 312836
- Size: 294.1 GB
- Data type: png
- Format: Knowledge graph using NetworkX with alpaca text box

### Schema

The png is in the **dbytes** column:

```
{
    "dbytes": "binary",
    "dbytes_len": "int64",
    "dbytes_mb": "float64",
    "filename": "string",
    "path": "string",
    "repo": "string",
    "type": "string"
}
```

### How to use the dataset

```python
from datasets import load_dataset

ds = load_dataset("matlok/python-image-copilot-training-using-class-knowledge-graphs-2024-01-27", data_dir="files")
```